Abstract--Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods. We note, however, that a CS method is not always readily available for Defect classifiers are widely used by many large software corporations a given classifier. Defect classifiers are commonly and deep neural networks do not have a widely accepted CS interpreted to uncover insights to improve software quality. Therefore it is the feature importance ranks of different classifiers is pivotal that these generated insights are reliable. Such CA methods measure the contribution of each feature a feature importance method to compute a ranking of feature towards a classifier's predictions. These measure the contribution of each feature by effecting changes to feature importance ranks reflect the order in which the studied that particular feature in the dataset and observing its impact on features contribute to the predictive capability of the studied the outcome. The primary advantage of CA methods is that they classifier [14].

We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of background features, we propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y with respect to selecting different background features. To this end, we propose a statistic V measuring the coefficient's variability. We prove, subject to a symmetry assumption for the background influence, that V converges to zero if and only if X contains no causal drivers. In experiments with simulated data, the method outperforms state of the art algorithms. Further, we report encouraging results for real-world data. Our approach aligns with the general belief that causal insights admit better generalization of statistical associations across environments, and justifies similar existing heuristic approaches from the literature.

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Scientists are working hard to enable artificial intelligence (AI) to identify and reduce the impact of human biases. Artificial intelligence algorithms are meant to replicate the working mechanism of the human brain for optimizing organizational activities. Unfortunately, while we have been able to get closer to actually recreating human intelligence artificially, AI also displays another distinctively human trait- prejudice against someone based on their race, ethnicity or gender. Bias in AI is not exactly a novel concept. Examples of biased algorithms in healthcare, law enforcement and recruitment industries have been uncovered recently as well as in the past.

Tata Consultancy Services, India's largest IT giant, announced on Thursday that it would expand its operations in New Jersey by recruiting 1,000 new employees by 2023 to fulfil the growing demand from customers to transform their businesses digitally. TCS will increase the scope of its STEM (Science, Technology, Engineering and Mathematics) and computer science education programmes in New Jersey by 25%, boosting teacher training and student programmes and creating a pipeline of local IT talent for the state to a statement. "TCS to expand STEM education programs in New Jersey and add 1,000 new employees by 2023," the company statement said. TCS' Edison Business Center in New Jersey serves more than 100 customers and is one of the company's 30 facilities in the United States. In the state, the corporation employs over 3,700 people who provide IT and consulting services to various industries, utilizing technologies including artificial intelligence, machine learning, cloud computing, and enterprise software.

Long-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model. Modern machine learning success stories often have one thing in common: they use methods that scale gracefully with ever-increasing amounts of data. This is particularly clear from recent advances in sequence modeling, where simply increasing the size of a stable architecture and its training set leads to qualitatively different capabilities.1 Meanwhile, the situation in reinforcement learning has proven more complicated. While it has been possible to apply reinforcement learning algorithms to large–scale problems, generally there has been much more friction in doing so.

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